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IBM C1000-177 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Development Tools and Techniques | 13% | - Work with structured and unstructured data formats - Navigate IBM watsonx.ai, Watson Studio, and Jupyter environments - Select appropriate statistical and modeling techniques - Use Python and libraries (Pandas, NumPy, Matplotlib, Scikit-learn) |
| Evaluate the Business Problem | 16% | - Identify appropriate analytical tools and methodologies - Define project scope and success criteria - Formulate testable hypotheses - Translate business objectives into data science/ML/AI solutions |
| Model Selection, Training, Evaluation, and Presentation | 17% | - Split data into training, validation, and test sets - Choose appropriate machine learning algorithms - Train and tune model parameters - Evaluate performance using correct metrics - Interpret results and communicate insights to stakeholders - Apply responsible AI and bias mitigation principles |
| Perform Exploratory Data Analysis | 21% | - Apply descriptive statistics and summary metrics - Use visualization techniques to identify patterns and relationships - Assess data quality and suitability for modeling - Analyze statistical distributions and correlations - Detect missing values, anomalies, and outliers |
| Pre-Processing and Feature Engineering | 33% | - Handle missing values and imbalanced data - Integrate data from multiple sources - Apply categorical and numerical encoding techniques - Select relevant features and reduce dimensionality - Clean and normalize datasets - Perform feature transformation and scaling |
